23 papers · ranked by Valyu relevance
Jana B. Jarecki, Jolene H. Tan, Mirjam A. Jenny
The term process model is widely used, but rarely agreed upon. This paper proposes a framework for characterizing and building cognitive process models. Process models model not only inputs and outputs but also model the ongoing information transformations at a given level of abstraction. We argue that the following…
Ahmed BaniMustafa, Nigel Hardy
This work demonstrates the execution of a novel process model for knowledge discovery and data mining for metabolomics (MeKDDaM). It aims to illustrate MeKDDaM process model applicability using four different real-world applications and to highlight its strengths and unique features. The demonstrated applications…
Nikola Simidjievski, Ljupčo Todorovski, Sašo Džeroski, Bin Liu
Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an…
Michael Winter, Rüdiger Pryss, Matthias Fink, Manfred Reichert
Process models constitute crucial artifacts in modern information systems and, hence, the proper comprehension of these models is of utmost importance in the utilization of such systems. Generally, process models are considered from two different perspectives: process modelers and readers. Both perspectives share…
Daniele Pessina, Tony Tian, Oliver Watson, Jerry Y. Heng + 1 more
Modelling complex crystallisation processes remains challenging due to limited experimental datasets, high measurement noise, and the need for generalisability across varying operating conditions. Neural Ordinary Differential Equations (NODEs) and transfer learning (TL) offer promising tools to overcome these…
Barbara Weber, Jakob Pinggera, Victoria Torres, Manfred Reichert
Process model quality has been an area of considerable research efforts. In this context, the correctness-by-construction principle of change patterns provides promising perspectives. However, using change patterns for model creation imposes a more structured way of modeling. While the process of process modeling (PPM)…
Sabah Al‐Fedaghi
—This paper presents a flow-based methodology for capturing processes specified in business process modeling. The proposed methodology is demonstrated through re-modeling of an IBM Blueworks case study. While the Blueworks approach offers a well-proven tool in the field, this should not discourage workers from…
Julia Baß, Michael Winter, Rüdiger Pryss, Manfred Reichert + 1 more
'Daniele Corbo'] The study of complex process models often encounters challenges in terms of comprehensibility. This paper explores using modularization as a strategy to mitigate such challenges, notably the reduction in complexity. Previous research has delved into the comprehensibility of modularized process models…
Jovan Tanevski, Ljupčo Todorovski, Sašo Džeroski
Background Identifying a proper model structure, using methods that address both structural and parameter uncertainty, is a crucial problem within the systems approach to biology. And yet, it has a marginal presence in the recent literature. While many existing approaches integrate methods for simulation and parameter…
Sabah Al‐Fedaghi
The software development life cycle (SDLC) is a procedure used to develop a software system that meets both the customer's needs and real-world requirements. The first phase of the SDLC involves creating a conceptual model that represents the involved domain in reality. In requirements engineering, building such a…
Philippe De Meyer, Jan Claes
Purpose - The rising interest in the construction and the quality of (business) process models resulted in an abundancy of emerged research studies and different findings about process model quality. The lack of overview and the lack of consensus hinder the development of the research field. The research objective is…
Jakob Pinggera, Pnina Soffer, Stefan Zugal, Barbara Weber + 4 more
'Matthias Weidlich' 'Dirk Fahland' 'Hajo A. Reijers' 'Jan Mendling'] Abstract. Research on quality issues of business process models has recently begun to explore the process of creating process models. As a consequence, the question arises whether different ways of creating process models exist. In this vein, we…
Authors not listed
Rapid and robust simulation of chemical processes is critical to conduct process design, optimization, techno-economic analysis, and sustainability analysis. Yet, efficiently solving simulation models remains a challenge due to the highly coupled and nonlinear nature of the underlying algebraic equations that capture…
Johannes Hemmerich, Niklas Tenhaef, Wolfgang Wiechert, Stephan Noack
Quantitative characterization of biotechnological production processes requires the determination of different key performance indicators (KPIs) such as titer, rate and yield. Classically, these KPIs can be derived by combining black-box bioprocess modelling with non-linear regression for model parameter estimation.…
Authors not listed
Microalgae-based tertiary wastewater treatment has the potential to meet stringent effluent phosphorus limits, with the added benefit of producing a marketable feedstock. However, the lack of validated mechanistic models and their implementation in process simulators have limited the adoption of this technology. In…
Aaron Liu, Myeongyeon Lee, Rahul Venkatesh, Jessica Bonsu + 4 more
Polymer-based semiconductors and organic electronics encapsulate a significant research thrust for informatics-driven materials development. However, device measurements are described by a complex array of design and parameter choices, many of which are sparsely reported. For example, the mobility of a polymer-based…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Rob H. Bemthuis
Process mining is increasingly adopted in modern organizations, producing numerous process models that, while valuable, can lead to model overload and decision-making complexity. This paper explores a multi-criteria decision-making (MCDM) approach to evaluate and prioritize process models by incorporating both…
Peter Sagmeister, Lukas Melnizky, Jason Williams, C. Oliver Kappe
In modern pharmaceutical research, the demand for expeditious development of synthetic routes to active pharmaceutical ingredients (APIs) has led to a paradigm shift towards data-rich process development. Conventional methodologies en-compass prolonged timelines for reaction and analytical model developments. Both…
Alexander L.R. Lubbock, Carlos F. Lopez
Computational modeling has become an established technique to encode mathematical representations of cellular processes and gain mechanistic insights that drive testable predictions. These models are often constructed using graphical user interfaces or domain-specific languages, with SBML used for interchange. Models…
Helle W. van den Maagdenberg, Martin Šícho, David Alencar Araripe, Sohvi Luukkonen + 9 more
Building reliable and robust quantitative structure-property relationship (QSPR) models is a challenging task. First, the experimental data needs to be obtained, analyzed and curated. Second, the number of available methods is continuously growing and evaluating different algorithms and methodologies can be arduous.…
Filipe Gouveia, Inês Lynce, Pedro T. Monteiro
Complex cellular processes can be represented by biological regulatory networks. Computational models of such networks have successfully allowed the reprodution of known behaviour and to have a better understanding of the associated cellular processes. However, the construction of these models is still mainly a manual…
Authors not listed
Accurate prediction of battery behavior under different dynamic operating conditions is critical for both fundamental research and practical applications. However, the diversity of emerging materials and cell architectures presents significant challenges to the generalizability of conventional prognostic approaches.…